The cyclic load service life prediction method of copper forgings in construction machinery usually adopts the fatigue life prediction model. The fatigue life of copper forgings is evaluated by considering the fatigue strength, stress concentration coefficient, load history and other factors. Here is a common method:
- Determine the fatigue strength curve of the material: Obtain the fatigue strength curve of the material through experiments or literature review, that is, the fatigue strength data under different stress levels.
- Determine the stress concentration coefficient: for the parts with stress concentration, it is necessary to calculate the stress concentration coefficient, which reflects the influence of stress concentration on the fatigue life of the material. It can be calculated using methods such as formulas or finite element analysis.
- Obtain load history: Through actual working condition test or simulation analysis, obtain the load history of copper forgings in the use process, including amplitude, frequency and load ratio and other parameters.
- Calculate the equivalent stress amplitude: according to the load history and stress concentration coefficient, calculate the equivalent stress amplitude of copper forgings, that is, the stress amplitude that produces the same fatigue damage as the actual working condition under the given load.
- Use the fatigue life prediction model: According to the selected fatigue life prediction model, the equivalent stress amplitude is substituted into the model, and the fatigue strength curve of the material is considered to predict the cyclic load service life of copper forgings.
It should be noted that when predicting the service life of cyclic load, other factors should also be taken into account, such as the impact of temperature, environmental corrosion, etc., on the fatigue performance of materials, and the safety factor requirements under actual working conditions. Therefore, in the actual project, it is recommended to combine a variety of methods and experience for comprehensive analysis and evaluation to ensure the accuracy and reliability of the predicted results.
